blackImproving FTR from LaMa. Although LaMa can produce high-quality inpainted results, the training of LaMa is quite unstable even with the EMA as compared in Fig. REF (a) on the Places2 subset. We expose that such an unstable training suffers from the resizing way of PatchGAN [1]}. For the patch-wise fake loss optimization of the discriminator, LaMa leverage the nearest resizing, where some partially masked patches are seemed as unmasked ones as in Fig. REF (d). Instead, we find that using maxpool resizing as Fig. REF (c) can greatly stabilize the training process. It means that partially masked patches should be considered as `fake' patches rather than `real' ones for inpainting.
Besides, we upgrade all activation in the generator of FTR to Swish [2]}, which has been shown to be effective in many generation works [3]}, [4]}. Detailed ablation studies of the improved FTR are shown in Tab. REF .
